DSFD: Dual Shot Face Detector

Dual Shot Face Detector

1、Description

DSFD for face detection in general scene with high detection rate

2、Prepare data

1、download WIDER Face Dateset， locate /opt/npu/

2、data process perform the following create data for train

python prepare_wider_data

3、Train

Note: pillow recommends installing a newer version. If the corresponding torchvision version cannot be installed directly, you can use the source code to install the corresponding version. The source code reference link: https://github.com/pytorch/vision，

Suggestion the pillow is 9.1.0 and the torchvision is 0.6.0 1、pretrained weight

download [pretrained weights](链接：https://pan.baidu.com/s/1qbQsOcgD3vuJ5m3Jnu6HTw 提取码：vbo9)

2、train full1p get train_1p log

bash ./test/train_full_1p.sh

3、train 1p performance

bash./test/train_performance_1p.sh

4、train full 8p get train_8p log

bash ./test/train_full_8p.sh

5、train 8p performance

bash ./test/train_performance_8p.sh 

6、use resume training

#enable resume training
add --resume "path/to/checkpoint" to .sh

4、Data inference

1.download wider_face_test.mat and wider_face_val.mat in /tools/infer_tools
2.cd /tools/infer_tools
python wider_face_test.py

5、Evalution

1、do setup first

cd /tools/eval_tools
python setup.py build_ext --inplace

2、download ground_truth and unzip to /tools/eval_tools

3、get data evaluation result

python evaluation.py

6、Demo

python demo.py --network 'resnet152'

7、DSFD training result

Acc	Npu_numbers	epochs	AMP_type
-	1	1	O2
E:0.9368 M:0.9282 H:0.8460	8	100	O2

Reference:

	Acc
参考精度	E:0.951 M:0.936 H:0.837
GPU 8P 自测精度	E 0.9473, M 0.9362, H 0.8651

Statement

For details about the public address of the code in this repository, you can get from the file public_address_statement.md